The present invention relates to automatic speech recognition (ASR), and more specifically, this invention relates to adjusting a deep neural network (DNN) acoustic model used in ASR.
Deep neural network (DNN) acoustic models are frequently used in the performance of automatic speech recognition (ASR). However, current methodologies for adapting DNN acoustic models to new test conditions suffer from covariate shift which arises from a distribution mismatch between training data and test data.